Marketing analytics : a practical guide to real marketing science
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- Publication date
- 2015
- Topics
- Marketing research, Marketing
- Publisher
- London : Philadelphia : Kogan Page
- Collection
- internetarchivebooks; nationaluniversity; inlibrary; printdisabled
- Contributor
- Internet Archive
- Language
- English
- Item Size
- 371.3M
xv, 232 pages : 24 cm
"Mike Grigsby provides business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing challenges, such as pulling a targeted list, segmenting data, testing campaign effectiveness, and forecasting demand. Assuming no prior knowledge, Marketing Analytics introduces concepts relating to statistics, marketing strategy, and consumer behavior and then works through a series of problems by providing various data modeling options as solutions. By using this format of presenting a problem and multiple ways to solve it, this book both makes marketing science accessible to beginners and aids the more experienced practitioner in understanding the more complex aspects of data analytics to refine their skills and compete more effectively in the workplace"--
"Marketing Analytics arms business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing problems, from pulling a targeted list and segmenting data to testing campaign effectiveness and forecasting demand. Assuming no prior knowledge, this book outlines everything practitioners need to 'do' marketing science and demonstrate value to their organization. It introduces concepts relating to statistics, marketing strategy and consumer behaviour and then works through a series of marketing problems in a straightforward, jargon-free way. It demonstrates solutions for various data modeling scenarios and includes full workings and critical analyses to reinforce the key concepts. By starting with the marketing problem and then sharing a series of data modeling options on how to solve it, Marketing Analytics both makes marketing science accessible for beginners and aids the more seasoned practitioner in getting to grips with the trickier technical aspects of data analytics to refine their marketing skills and toolkit and compete more effectively in the marketplace. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"--
Part One: Overview. 1. A (little) statistical review -- 2. Brief principles of consumer behaviour and marketing strategy -- Part Two: Dependent variable techniques. 3. Modelling dependent variable techniques (with one equation): what are the things that drive demand? -- 4. Who is most likely to buy and how do I target? -- 5. When are my customers most likely to buy? -- 6. Modelling-dependent variable techniques (with more than one equation) -- Part Three: Inter-relationship techniques. 7. Modelling inter-relationship techniques: what does my (customer) market look like? -- 8. Segmentation: tools and techniques -- Part Four: Other. 9. Marketing research -- 10. Statistical testing: how do I know what works? -- Part Five: Capstone. 11 Capstone: focusing on digital analytics -- Part Six: Conclusion. 12. The finale: what should you take away from this? Any other stories/soap box rants?
Includes bibliographical references and index
"Mike Grigsby provides business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing challenges, such as pulling a targeted list, segmenting data, testing campaign effectiveness, and forecasting demand. Assuming no prior knowledge, Marketing Analytics introduces concepts relating to statistics, marketing strategy, and consumer behavior and then works through a series of problems by providing various data modeling options as solutions. By using this format of presenting a problem and multiple ways to solve it, this book both makes marketing science accessible to beginners and aids the more experienced practitioner in understanding the more complex aspects of data analytics to refine their skills and compete more effectively in the workplace"--
"Marketing Analytics arms business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing problems, from pulling a targeted list and segmenting data to testing campaign effectiveness and forecasting demand. Assuming no prior knowledge, this book outlines everything practitioners need to 'do' marketing science and demonstrate value to their organization. It introduces concepts relating to statistics, marketing strategy and consumer behaviour and then works through a series of marketing problems in a straightforward, jargon-free way. It demonstrates solutions for various data modeling scenarios and includes full workings and critical analyses to reinforce the key concepts. By starting with the marketing problem and then sharing a series of data modeling options on how to solve it, Marketing Analytics both makes marketing science accessible for beginners and aids the more seasoned practitioner in getting to grips with the trickier technical aspects of data analytics to refine their marketing skills and toolkit and compete more effectively in the marketplace. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"--
Part One: Overview. 1. A (little) statistical review -- 2. Brief principles of consumer behaviour and marketing strategy -- Part Two: Dependent variable techniques. 3. Modelling dependent variable techniques (with one equation): what are the things that drive demand? -- 4. Who is most likely to buy and how do I target? -- 5. When are my customers most likely to buy? -- 6. Modelling-dependent variable techniques (with more than one equation) -- Part Three: Inter-relationship techniques. 7. Modelling inter-relationship techniques: what does my (customer) market look like? -- 8. Segmentation: tools and techniques -- Part Four: Other. 9. Marketing research -- 10. Statistical testing: how do I know what works? -- Part Five: Capstone. 11 Capstone: focusing on digital analytics -- Part Six: Conclusion. 12. The finale: what should you take away from this? Any other stories/soap box rants?
Includes bibliographical references and index
- Access-restricted-item
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- Addeddate
- 2023-07-11 22:24:13
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